MoAlB ceramic coatings were prepared on a 316 steel surface by atmospheric plasma spraying with different arc power levels. The phase composition, microstructure and wear resistance of coatings against GCr15 and Si₃N₄ counterparts were studied. The MoAlB ceramic decomposed and was oxidized to form MoB and Al₂O₃ during plasma spraying. With the increase of the arc power, MoAlB experienced more decomposition, but the coatings became denser. When the arc power increased from 30 to 36 kW, the wear rates of coatings against GCr15 and Si₃N₄ balls reduced by 91% and 78%, respectively. The characterization of wear tracks shows that when against GCr15 counterparts, the main wear mechanisms are abrasive and adhesive wear, and when against Si₃N₄ counterparts, fatigue and abrasive wear are dominant. The refinement of wear resistance by increasing arc power can be attributed to the improvement of density and adhesive strength among splats.

We detail the linear peridynamic wave equation with a nonlocal integral form based on the linear peridynamic and dynamic theory. Wave dispersion in an infinite maraging steel material is obtained by analyzing the linear peridynamic wave equation. The dispersion curves, group velocity, phase velocity, and other wave parameters of the shear and longitudinal waves in an infinite media are obtained using numerical methods. We obtained the optimal calculation parameters by analyzing the weight function, horizon, mesh size, and other numerical calculation parameters on the dispersion curve. We simulated the propagation of waves in an infinite media by applying these parameters into the peridynamic wave equation. We conclude that the wave model can generate waves that propagate in all directions with initial loads. The wavefront is an ellipsoid.

Laser welding quality forecast is highly significant during the laser manufacturing process. However, extracting the dynamic characteristics of the molten pool in the short laser welding process makes predicting of the welding quality in real time difficult. Accordingly, this study proposes a multimodel quality forecast (MMQF) method based on dynamic geometric features of molten pool to forecast the welding quality in real time. For extraction of geometric features of molten pool, an improved fully convolutional neural network is proposed to segment the collected dynamic molten pool images during the entire welding process. In addition, several dynamic geometric features of the molten pool are extracted by using the minimum enclosed rectangle algorithm with an evaluation of the performance by several statistical indexes. With regard to forecasting the welding quality, a nonlinear quadratic kernel logistic regression model is proposed by mapping the linear inseparable features to the high dimensional space. Experimental results show that the MMQF method can make an effective and stable forecast of welding quality. It performs well under small data and can satisfy the requirement of real-time forecast.